A codebook-free and annotation-free approach for fine-grained image categorization

Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual appearances. Most of the state-of-the-art basic-level object classification algorithms have difficulties in this challenging problem. One reason for this can be attributed to the popular codebook-based image representation, often resulting in loss of subtle image information that are critical for fine-grained classification. Another way to address this problem is to introduce human annotations of object attributes or key points, a tedious process that is also difficult to generalize to new tasks. In this work, we propose a codebook-free and annotation-free approach for fine-grained image categorization. Instead of using vectorquantized codewords, we obtain an image representation by running a high throughput template matching process using a large number of randomly generated image templates. We then propo...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Bangpeng Yao, Gary R. Bradski, Fei-Fei Li
• Keywords: Computer Vision, Cvpr 2012, Image Representation, Image Templates, Object Attributes